The present invention relates to an object detection system using an image-pickup device. More particularly, it relates to an intruding-object surveillance method and an intruding-object surveillance apparatus that, by processing the image signals, allow an object to be automatically detected which has intruded within a detection target region.
Conventionally, the remote-monitor-scheme surveillance systems using the image-pickup device such as a TV camera (television camera) have been widely used. Moreover, many of these systems are the systems based on the so-called manned surveillance scheme by which surveillance personnel perform the surveillance while watching the images displayed on a monitor.
In these manned-surveillance-scheme surveillance systems, however, the surveillance personnel are required to watch all the time the images displayed on the monitor. This is needed to identify an intruding object in real time, such as a human or an automobile that is coming into a surveillance target region. Accordingly, so much burden is imposed on the surveillance personnel. In addition thereto, it is impossible to avoid an occurrence of oversight, since there exists a limit to the human's concentration. Consequently, there exists problem in reliability.
In view of this situation, in recent years, it has been getting more and more strongly requested to implement the so-called automatic-detection-scheme surveillance system instead of the manned surveillance scheme as described above. In this automatic-detection-scheme surveillance system, an intruding object is automatically detected by the signal processing of the images picked up by a TV camera, thereby allowing the execution of a predetermined notice-giving or alarm-issuing procedure step.
By the way, the implementation of the automatic-detection-scheme surveillance system like this necessitates a function of using a certain surveillance scheme so as to detect, from the image signals, a surveillance target object that should be assumed to be an intruding object. As one example of the surveillance scheme at this time, there exists a method referred to as “subtraction method”. This subtraction method, which is disclosed in, e.g., JP-A-7-79429 and the like, has been widely used conventionally.
Here, this subtraction method is the following one: Of images picked up by a TV camera, an image where there exists none of an intruding object to be detected is selected and stored in advance as a reference background image. Next, a comparison is made between an input image (i.e., image outputted from the TV camera) and this reference background image, thereby determining a subtraction or difference between the luminance values on each pixel basis. Moreover, the subtraction values are subjected to a difference-judging processing under a predetermined threshold value, thereby generating a binarized image. Finally, if, within the binarized image, there appears a region that has exceeded a predetermined size, the region is assumed to be an intruding object, then being detected.
In the case of the subtraction method, enhancing the detection accuracy of an intruding object requires the existence of several conditions to be considered as parameters. As the representative parameters among them, there exist the above-described threshold value and a masking region that will be described later.
At first, explanation will be given below concerning the threshold value. FIG. 4A illustrates an example of the case where the threshold value set in advance is a proper one. In this case, as a result of performing the threshold-value processing to a subtraction between an input image 401 and the reference background image, because of the proper threshold value, a person existing within the input image 401 has been able to be correctly detected as an intruding object 401a. 
Meanwhile, FIG. 4B illustrates an example of the case where the set threshold value is smaller than the proper one. In this case, because of the small threshold value, the following detection result has been obtained: Although no subtraction should occur in grass/tree portions between an input image 402 and the reference background image, the grass/tree portions have been also detected as intruding objects. As a result, three types of intruding objects, i.e., 402a, 402b, and 402c, have been assumed to exist within the input image 402.
Conversely, FIG. 4C illustrates an example of the case where the set threshold value is larger than the proper one. In this case, because of the large threshold value, the following detection result has been obtained: In the region where there exists the object to be detected, it is impossible to detect a portion in which a subtraction between an input image 403 and the reference background image is small. As a result, the one person, who, originally, should be detected as the intruding object 401a as is illustrated in FIG. 4A, has been detected as split plural objects, i.e., 403a and 403b, 
The explanation given so far makes it possible to easily understand the following facts: In the subtraction method, the setting of the threshold value is one of the significant factors for determining the detection performance and detection accuracy of an object. Simultaneously, the correct setting of this threshold value requires a high-level proficiency.
Next, explanation will be given below regarding the masking region. Here, at first, FIG. 5A illustrates an example of an input image 501 in the case where, similarly to the case in FIG. 4A, an intruding object 501a has been able to be normally detected. On the other hand, FIG. 5B illustrates an example of the following case: Objects that, originally, are supposed to have already become the background within an input image 502, e.g., grasses and trees existing within the input image 502, have moved for some reason or other (namely, e.g., have been swayed by the wind).
Moreover, in this case in FIG. 5B, in the grass/tree portions as well, there occur differences between the input image 502 and the reference back-ground image despite the fact that no intruding object exists except an object (i.e., person) 502a. As a result, three types of objects, i.e., 502a, 502b, and 502c, will be assumed to exist within the input image 502.
In view of this situation, in the case like this, the following mask processing becomes necessary: As illustrated in FIG. 5C, at first, a masking region 503b is set within an input image 503 with respect to a region where there exist objects that will be moved by the wind, e.g., grasses and trees. Next, the inside of this masking region is processed as an insensitive region (i.e., region in which no judgment is made concerning the existence of an object). For example, U.S. patent application Ser. No. 10/387,433 relates to a mask processing.
Incidentally, this mask processing, which is applicable not only to the subtraction method but also to various object detection methods, is effective in enhancing the object detection performance.
In the case of this mask processing, however, no sufficient performance can be acquired unless the setting of the masking region has been properly implemented. In the case of, e.g., the input image 503 in FIG. 5C, the masking region must be set as follows: Excluding a region where an intruding object 503a should appear, only the objects such as the grasses and trees are correctly masked which, by being moved by the wind or the like, are in a danger of being falsely detected as the intruding objects.
Accordingly, it becomes possible to easily understand the following facts: In the subtraction method, the setting of the masking region is also one of the significant factors for determining the detection performance and detection accuracy of an object. Simultaneously, the correct setting of this masking region also requires a high-level proficiency.
Incidentally, so far, explanation has been given employing the subtraction method as the example. However, not only in the subtraction method but also in the other object detection methods, maintaining the object detection performance necessitates the parameters such as the threshold value and the masking region, and also necessitates the correct setting of these parameters.
In view of the above-described situation, in the automatic-detection-scheme surveillance system according to the prior art, when starting the operation of the surveillance system, it is quite natural that the operator set the parameters to be proper parameters. What is more, during the system operation as well, the operator repeatedly makes an adjustment appropriately, as required, thereby allowing the performance of the surveillance system to be all the time maintained as is specified in the specification thereof under the setting of the proper parameters.
The execution of this repeated adjustment, however, requires that the operator be obliged to always be present at the site of the surveillance system. If this is difficult, maintaining the performance of the surveillance system also becomes difficult. Consequently, there exist problems in the detection accuracy and detection reliability of an intruding object.
Namely, unless the operator had happened to be present just at the very site when there actually occurred a false or erroneous detection of an object, it is difficult to make the judgment as to whether or not the setting of the parameters is proper.
Accordingly, in order to allow the performance of the surveillance system to be properly maintained all the time under the setting of the proper parameters, it is required for the operator to wait for a false detection to occur and then to correct the parameters.
Conventionally, this requirement has forced the operator to standby all the time (i.e., always present) at the installment site of the surveillance system and to wait for the detection of an intruding object by the surveillance system. Here, however, it is completely unpredictable at what point-in-time this detection will occur. Consequently, as described above, maintaining the performance of the surveillance system becomes difficult. As a result, it turns out that there occur the problems in the detection accuracy and detection reliability of the intruding object.